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122 results for “sea level rise”
DynaRev - Dynamic Coastal Protection: Resilience of Dynamic Revetments Under Sea Level Rise
<p>This dataset contains processed data from the DynaRev experiments carried out in the Large Wave Flume (Grosser Wellenkanal, GWK) from 14-08-2017 to 29-09-2017 as a Transnational Access project within the EU funded project HYDRALAB+ (654110). The dataset provided under this DOI includes the post-processed data detailed in the article submitted to Scientific Data titled "High-resolution, prototype-scale laboratory measurements of nearshore wave processes and morphological evolution of a sandy beach with and without a dynamic cobble berm revetment"</p> <p>The overall aim of this project was to construct a prototype-scale beach and investigate the response of the beach to a rising sea level and storms with and without a cobble berm dynamic revetment structure (a gravel or shingle ridge placed around the wave runup limit). The response of two beach configurations was investigated under erosive wave conditions (Hs=0.8m, Tp=6.0s) and a total sea-level rise, SLR = 0.4 m:</p> <p>i) an unmodified sand beach with an initially plane slope of 1:15, and<br> ii) a natural beach profile with a dynamic revetment installed at the location of the natural berm before imposing SLR.</p> <p>SLR was imposed in 4 steps of 0.1 m with an initial water depth of 4.5 m above the flume base. Each phase ran for a total of 58 hours, after which resilience testing under storm waves and accretive conditions was undertaken for a further 12 hours in each case.</p> <p>The changing profile of the sand beach and revetment was monitored throughout the experiments using a traditional profiler and a LiDAR array. Additional hydrodynamic measurements were obtained at the location of the offshore sandbar to investigate the process of bar formation and migration with SLR.</p> <p>The model set-up, experimental program and data structure are explained in the submitted Scientific Data paper. The data files are organised as detailed in "DynaRev_Data_Structure.doc".</p> <p> </p>
Supporting data for "Synthesizing long-term sea level rise projections - the MAGICC sea level model v2.0"
<p>This is supporting data and configuration information to reproduce results from the MAGICC sea level model (DOI: 10.5281/zenodo.572395) presented in Nauels et al. (2017), using version 7.0 beta of the simple climate carbon-cycle model MAGICC (Meinshausen et al. 2011). For a compiled or source code version of MAGICC including the sea level model (git hash: c5c4e05518ed99f2bd53e2c6e68d238bbd6f17ec), please contact alexander.nauels@climate-energy-college.org.</p> <p>MAGICC input data and CMIP5 reference datasets are provided as a zip-file.</p> <p><br> REFERENCE DATASETS</p> <p>For MAGICC version 7.0 beta, the ocean model has been updated to emulate CMIP5 ocean temperatures and thermal expansion. The calibration results shown in Nauels et al. (2017) are based on potential ocean temperature (thetao) and thermal expansion (zostoga) reference datasets that are provided the 'data' directory. Reference datasets for the other sea level components have to be requested from the authors of the corresponding studies (Marzeion et al. 2014, Fettweis et al. 2013, Nick et al. 2013, Ligtenberg 2013, Levermann 2014). All relevant CMIP5 MAGICC input (.IN) is provided in the 'run' directory.</p> <p><br> MAGICC MODEL CONFIGURATION</p> <p>To customize a MAGICC run, namelist entries have to be modified in the configuration file 'MAGCFG_USER.CFG'. In order to select a CMIP5 model specific MAGICC ocean calibration, the model setup has to be called with the 'FILE_TUNINGMODEL_XX' entry, e.g. 'OCNTUNE_CCSM4' for the CCSM4 model. Automatically, the corresponding initial ocean temperature profile and model specific ocean layer area fractions will be applied. For prescribing the respective surface air temperatures, the namelist flag 'CORE_PRESCRTEMP_APPLY' has to be set to 1, with the model specific temperature dataset defined by 'FILE_PRESCR_SURFACETEMP', e.g. 'CORE_PRESCRTEMP_CMIP5_CCSM4_RCP85.IN'. The following MAGICC namelist entries have to be adapted in order to fully reproduce CMIP5 consistent results presented in Nauels et al. (2017): </p> <p>[...]<br> e.g. FILE_TUNINGMODEL_1 = "OCNTUNE_CCSM4",<br> [...]<br> CORE_SWITCH_TEMPADJUST_OCN2ATM = 1,<br> CORE_SWITCH_OCN_TEMPPROFILE = 2,<br> CORE_SWITCH_OCN_AREAFACTOR = 1,<br> CORE_PRESCRTEMP_APPLY = 1,<br> e.g. FILE_PRESCR_SURFACETEMP = "CORE_PRESCRTEMP_CMIP5_CCSM4_RCP85.IN",<br> [...]<br> OUT_TEMPERATURE = 1,<br> OUT_TEMPOCEANLAYERS = 1,<br> OUT_SEALEVEL = 1,<br> OUT_PARAMETERS = 1,<br> [...]<br> OUT_ASCII_BINARY = "ASCII",<br> [...]</p> <p>MAGICC output is stored in the 'out' directory. Depending on the flag 'OUT_ASCII_BINARY', either ASCII or BINARY files are produced for the output parameters which are set to 1 in the namelist, e.g. 'OUT_SEALEVEL = 1'. </p> <p><br> MAGICC SEA LEVEL MODEL LICENSE</p> <p>This source code of the MAGICC sea level model is distributed under a Creative Commons Attribution-ShareAlike 4.0 license (https://creativecommons.org/licenses/by-sa/4.0/legalcode).</p> <p><br> MAGICC PARENT MODEL LICENSES</p> <p>The MAGICC executable is provided under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license (https://creativecommons.org/licenses/by-nc-sa/3.0/). The MAGICC source code is available under a separate license agreement. Any derivatives have to be fed back to the MAGICC developers, so that users of future MAGICC versions can have the benefit of applying the model alterations, enhancements etc. Furthermore, we would like you to provide feedback, bug reports and development suggestions.</p> <p><br> REFERENCES</p> <p>Fettweis, X., Franco, B., Tedesco, M., van Angelen, J. H., Lenaerts, J. T. M., van den Broeke, M. R., and Gallée, H.: Estimating the Greenland ice sheet surface mass balance contribution to future sea level rise using the regional atmospheric climate model MAR, The Cryosphere, 7, 469–489, 2013.</p> <p>Levermann, A.,Winkelmann, R., Nowicki, S., Fastook, J. L., Frieler, K., Greve, R., Hellmer, H. H., Martin, M. A., Meinshausen, M., Mengel, M., Payne, A. J., Pollard, D., Sato, T., Timmermann, R., Wang, W. L., and Bindschadler, R. A.: Projecting Antarctic ice discharge using response functions from SeaRISE ice-sheet models, Earth Syst. Dynam., 5, 271–293, 2014.</p> <p>Ligtenberg, S. R. M., van de Berg, W. J., van den Broeke, M. R., Rae, J. G. L., and van Meijgaard, E.: Future surface mass balance of the Antarctic ice sheet and its influence on sea level change, simulated by a regional atmospheric climate model, Climate Dynamics, 41, 867–884, 2013.</p> <p>Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 1: Model description and calibration, Atmospheric Chemistry and Physics, 11, 1417–1456, 2011.</p> <p>Nauels, A., Meinshausen, M., Mengel, M., Lorbacher, K., and Wigley, T. M. L.: Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0, Geosci. Model Dev., 2017.</p> <p>Nick, F. M., Vieli, A., Andersen, M. L., Joughin, I., Payne, A., Edwards, T. L., Pattyn, F., and van de Wal, R. S. W.: Future sea-level rise from Greenland’s main outlet glaciers in a warming climate, Nature, 497, 235–238, 2013.</p>
Data: Salt marsh litter quality and decomposition under sea-level rise scenarios: from leaves to fine absorptive roots
<p>litter chemical characteristic in salt marshes, including fine absorptive roots, fine transportive roots, rhizomes and leaves. </p> <p>mass loss of litter and chemical characteristics of those litter under sea level scenarios (manipulated in situ)</p>
Data and code for: Sea level rise causes shorebird population collapse before habitat drowns
<p>Sea level rise causes habitat loss and is considered to be a key threat to coastal species globally. Sea level rise also reduces habitat quality, potentially threatening populations already before habitat drowns and is lost. The extent and timing of changes in habitat quality for wildlife actively adapting to sea level rise, and how this affects population numbers under different emission scenarios, is unknown. Here, we combine long-term field data with models of sea level rise, marsh geomorphology, adaptive behaviour, and population dynamics to show that habitat quality is already declining on three islands due to increased flooding of shorebird nests. Also, population collapses are projected well before habitat drowns. Habitat loss, a widely used proxy, thus severely underestimates population impacts of sea level rise and coastal species will suffer much sooner than previously thought. Despite shorebirds adapting by moving to higher grounds, sea level rise will result in up to 79% fewer birds in a century, eventually leading to extinction in their prime habitat. Local gas mining exacerbates matters, as deep soil subsidence makes habitat even more vulnerable to sea level rise, effectively halving the window of opportunity for conservation action. Climate change ultimately jeopardizes the biodiversity value of this UNESCO World Heritage Area, and nature management needs to take this long-term perspective on board by in the short-term, boosting the accretion of tidal marshes or developing flood-safe alternative habitat elsewhere.</p>
Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines
<p><strong>Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines</strong></p> <p><strong>Versions:</strong></p> <p>Version 1.1 This one:</p> <ul> <li>updated region names</li> </ul> <p>Version 1.0 <a href="https://doi.org/10.5281/zenodo.5951626">https://doi.org/10.5281/zenodo.5951626</a></p> <p>This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines.</p> <p>The report can be found on <a href="https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html">https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html</a></p> <p>An interactive tool to study the observations, trajectories, and scenarios can be accessed from <a href="https://sealevel.nasa.gov/task-force-scenario-tool">https://sealevel.nasa.gov/task-force-scenario-tool</a></p> <p>Frequently-asked questions: <a href="https://sealevel.nasa.gov/faq/16/">https://sealevel.nasa.gov/faq/16/</a></p> <p><strong>Authors</strong></p> <ul> <li>William V. Sweet, NOAA National Ocean Service</li> <li>Benjamin D. Hamlington, NASA Jet Propulsion Laboratory</li> <li>Robert E. Kopp, Rutgers University</li> <li>Christopher P. Weaver, U.S. Environmental Protection Agency</li> <li>Patrick L. Barnard, U.S. Geological Survey</li> <li>Michael Craghan, U.S. Environmental Protection Agency</li> <li>Gregory Dusek, NOAA National Ocean Service</li> <li>Thomas Frederikse, NASA Jet Propulsion Laboratory</li> <li>Gregory Garner, Rutgers University</li> <li>Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research</li> <li>John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory</li> <li>Eric Larour, NASA Jet Propulsion Laboratory</li> <li>Doug Marcy, NOAA National Ocean Service</li> <li>John J. Marra, NOAA National Centers for Environmental Information</li> <li>Jayantha Obeysekera, Florida International University</li> <li>Mark Osler, NOAA National Ocean Service</li> <li>Matthew Pendleton, Lynker</li> <li>Daniel Roman, NOAA National Ocean Service</li> <li>Lauren Schmied, FEMA Risk Management Directorate</li> <li>William C. Veatch, U.S. Army Corps of Engineers</li> <li>Kathleen D. White, U.S. Department of Defense</li> <li>Casey Zuzak, FEMA Risk Management Directorate</li> </ul> <p><strong>Contents</strong></p> <p>This data and code set contains the following directories:</p> <p><em>Results</em></p> <p>The <code>Results</code> folder contains the resulting projections, trajectories and observations from the report.</p> <ul> <li><code>TR_global_projections.nc</code>: GMSL projections, trajectory, and observations</li> <li><code>TR_regional_projections.nc</code>: Regional observations, projections and trajectories</li> <li><code>TR_local_projections.nc</code>: Local observations, projections and trajectories</li> <li><code>TR_gridded_projections.nc</code>: Gridded projections</li> </ul> <p>These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including <a href="http://meteora.ucsd.edu/~pierce/ncview_home_page.html">ncview</a> and <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a>. Free NetCDF packages are available to directly import the data into <a href="https://github.com/Alexander-Barth/NCDatasets.jl">Julia</a> and <a href="https://unidata.github.io/netcdf4-python/">Python</a> code.</p> <p><em>Code</em></p> <p>The <code>Code</code> folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories.</p> <p>To run this code, you need <a href="https://julialang.org/">Julia</a>. The code requires the Julia packages <code>CSV</code>, <code>Interpolations</code>, <code>JSON</code>, <code>LoopVectorization</code>, <code>MAT</code>, <code>NCDatasets</code>, <code>NetCDF</code>, <code>Plots</code>, <code>XLSX</code>, <code>LinearAlgebra</code>, and <code>Statistics</code>. They can be installed by pressing <code>]</code> at the Julia REPL and typing:</p> <pre><code>add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics </code></pre> <p>This program also requires <a href="http://segal.ubi.pt/hector/">Hector</a>. Hector needs to be installed or compiled. In the file <code>Hector.jl</code> update the path to the Hector executable on lines 30 and 104.</p> <p>Run <code>Run_TR.jl</code> in the REPL or run <code>julia Run_TR.jl</code> from the command line to run the projections. The projections are then written to the <code>.\Data</code> directory.</p> <p>The folder contains the following files:</p> <ul> <li><code>Run_TR.jl</code>: This is the main routine that (eventually) calls all the functions to compute the projections.</li> <li><code>ConvertNCA5ToGrid.jl</code>: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code</li> <li><code>ProcessObservations.jl</code>: Reads and processes the tide-gauge and altimetry observations</li> <li><code>GlobalProjections.jl</code>: Reads and processes the GMSL observations and projections, and computes the trajectory</li> <li><code>RegionalProjections.jl</code>: Reads and processes the regional projections and computes the trajectories</li> <li><code>LocalProjections.jl</code>: Reads and processes the local projections at the tide-gauge locations and computes the trajectories</li> <li><code>GriddedProjections.jl</code>: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline</li> <li><code>SaveFigureData.jl</code>: Reads the results and writes text files for GMT</li> <li><code>Hector.jl</code>: Wrapper for <a href="http://segal.ubi.pt/hector/">Hector</a>, used to compute trends and uncertainties.</li> <li><code>Masks.jl</code>: Defines the region masks for each region.</li> </ul> <p><em>Data</em></p> <p>The <code>Data</code> directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following:</p> <p>Directories:</p> <ul> <li><code>ClimIdx</code>: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA <a href="https://psl.noaa.gov/data/climateindices/">Physical Sciences Laboratory (PSL)</a> and <a href="https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml">NOAA Climate Prediction Centre (CPC)</a></li> <li><code>NCA5_projections</code> Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.</li> </ul> <p>Files:</p> <ul> <li><code>basin_codes.nc</code>: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.</li> <li><code>CDS_monthly_1993_2020.nc</code>: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global">Copernicus Climate Data Store</a>. This dataset contains modified Copernicus Climate Change Service information [2020]</li> <li><code>enso_correction.mat</code>: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. <a href="https://doi.org/10.1029/2019GL086528">https://doi.org/10.1029/2019GL086528</a></li> <li><code>filelist_psmsl.txt</code>: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (<a href="http://www.psmsl.org/">PSMSL</a>), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. <a href="https://doi.org/:10.2112/JCOASTRES-D-12-00175.1">https://doi.org/:10.2112/JCOASTRES-D-12-00175.1</a>.</li> <li><code>GEBCO_bathymetry_05.nc</code>: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (<a href="https://www.gebco.net/">GEBCO</a>). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (<code>doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f</code>) The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>GIA_Caron_stats_05.nc</code>: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. <a href="https://doi.org/10.1002/2017GL076644">https://doi.org/10.1002/2017GL076644</a>. The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>global_timeseries_measures.nc</code>: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_ensembles.nc</code>: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_TPJAOS_5.0_199209_202106.txt</code>: Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 [Data set]. NASA Physical Oceanography DAAC. <a href="https://doi.org/10.5067/GMSLM-TJ151">https://doi.org/10.5067/GMSLM-TJ151</a>. This altimetry dataset uses the methods as described in Beckley, B. D., Callahan, P. S., Hancock, D. W., Mitchum, G. T., & Ray, R. D. (2017). On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series. Journal of Geophysical Research: Oceans, 122(11), 8371–8384. <a href="https://doi.org/10.1002/2017JC013090">https://doi.org/10.1002/2017JC013090</a></li> <li><code>grd_1992_2020.nc</code>: Seafloor deformation due to contemporary GRD effects based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>region_mask.nc</code>: Mask with the definition of all regions.</li> <li><code>US_tg_monthly.xlsx</code>: Tide gauge observations from the NOAA tide gauge network</li> </ul> <p><em>GMT</em></p> <p>This directory contains the <a href="https://www.generic-mapping-tools.org/">GMT</a> scripts to make Figures 1.2, 2.1, 2.2, 2.6, and A.1.2 from the report. To generate the figures, make sure GMT is installed and run the Shell script in each directory.</p>
Input and Output files for "Contributions to Streamflow and Sea Level Rise in High Mountain Asia from 2003-2009 Glacier Recession"
<p>All files below were prepared by Collin B. Lawrence. </p> <p>All ARCIDs correspond to the HydroSHEDS Dataset for Asia (Lehner et al., 2008). (http://www.hydrosheds.org/)</p> <p>GLDAS data are from the Global Land Data Assimilation System (Rodell et al., 2004). (https://ldas.gsfc.nasa.gov/gldas/)</p> <p>Q_JJA_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are averaged for the months of June, July, and August from the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>Q_annual_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are yearly averages for the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>phi_i_JJA is the accumulated subsurface and surface runoff for the CLM, MOSAIC, NOAH, and VIC model average. The June, July, and August output was averaged over the years 2003 – 2009. Column 1 is ARCID and the accumulated runoff is expressed in m<sup>3</sup> s<sup>-1</sup>.</p> <p>phi_i_JJA_err is the standard error of the model mean in phi_i_JJA.</p> <p>phi_g_JJA contains the accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for the months of June, July, August from 2003 - 2009.</p> <p>phi_g_JJA_err is the standard error of the model mean in phi_g_JJA.</p> <p>phi_g_annual contains the annually averaged accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for 2003 – 2009.</p> <p>lambda.csv contains the fraction of streamflow from glacier recession.</p>
Data supplementing article "Tidal response to sea-level rise in different types of estuaries: the importance of length, bathymetry, and geometry" submitted to Geophysical Research Letters
<p>These data supplement the article "Tidal response to sea-level rise in different types of estuaries: the importance of length, bathymetry, and geometry". </p> <p>contact: Jiabi Du, jiabi.du@gmail.com</p> <p>Below are descriptions of the data files included here:</p> <p>1. ReadMe.txt</p> <p>- the description of each model configuration is listed in this file, inlcuding the estuarine geometry type, length, and bathymetry type. </p> <p>2. model grid and surface elevation output</p> <p>- In each directory named after the grid_name, a grid file with .gr3 format is included. <br> The .gr3 file contains two parts: the location of each node (first part), and the node number for each triangle grid (second part).</p> <p>- Two sub_directory name 'base' and 'slr' contains the time series of water level. </p> <p>3. MatlaScript ExtractWL</p> <p>- a function named "f_extract_wl.m" are used to extract the station output</p> <p> </p>
Wave climate simulations for Denmark - for paper 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'
<p>This wave climate dataset are the results for the paper titled 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'.</p> <p>The operational wave forecasting service provided by DMI-WAM uses the WAM Cycle version 4.5.4, a third-generation spectral wave model. DMI-WAM is used for the wave climate simulations. The meteorological forcing used in this study was obtained from the regional climate model DMI-HIRHAM, developed by the Danish Meteorological Institute (DMI). It is a component of the CORDEX (Coordinated Regional Climate Downscaling Experiment) ensemble in Europe. Regarding the selection of the time frame and IPCC scenarios in our study, we adhered to the recommendations provided by municipalities. Municipalities are keenly interested in obtaining near-future wind wave data for the specific purpose of using them for risk management. Therefore, the examination of forthcoming weather extremes in the near future within the context of the high greenhouse gas emission scenario (RCP8.5 scenario) is of significance within this investigation. We conduct simulations that encompass two distinct time periods: the historical period spanning from 1976 to 2005, and the near-future period from 2041 to 2070. We analyse the WAM model results for wave climate under both present climate conditions (1976-2005) and future climate scenarios (2041-2070) under the RCP8.5 scenario. Furthermore, note that while our wave climate simulations provide valuable insights into the dynamics of wind-induced waves, the mean SLR is not explicitly taken into account. The mean SLR component is considered in the storm surge simulations.</p> <p>Description of files:</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.hist.final.max.nc</a> - Maximum sea level, significant wave height, wave length and slope for the historical period.</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.rcp85.final.max.MSLR35.nc</a> - Maximum sea level, significant wave height, wave length and slope for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.hist.final.max.nc</a> - Maximum wave setup for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.rcp85.final.max.MSLR35.nc</a> - Maximum wave setup for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.his.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.his.swh.98p.nc</a> - 2% exceedence of significant wave height for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.rcp8.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.rcp8.swh.98p.nc</a> - 2% exceedence of significant wave height for the RCP8.5 period.</p>
Future sea level rise in northwest Mexico is projected to decrease the distribution and habitat quality of the endangered Calidris canutus roselaari (Red Knot)
<p>Sea level rise (SLR) is one of the most unequivocal consequences of climate change, yet the implications for shorebirds and their coastal habitats is not well understood, especially outside of the north temperate zone. Here, we show that by the year 2050, SLR has the potential to cause significant habitat loss and reduce the quality of the remaining coastal wetlands in Northwest Mexico—one of the most important regions for Nearctic breeding migratory shorebirds. Specifically, we used species distribution modelling and a moderate SLR static inundation scenario to assess the effects of future SLR on coastal wetlands in Northwest Mexico and the potential distribution of <em>Calidris canutus roselaari </em>(Red Knot), a threatened long-distance migratory shorebird. Our results suggest that under a moderate SLR scenario, 55% of the current coastal wetland extent in northwest Mexico will be at risk of permanent submergence by 2050, and the high-quality habitat areas that remain will be 20% less suitable for <em>C. c. roselaari</em>. What is more, 8 out of the 10 wetlands currently supporting the largest numbers of <em>C. c. roselaari</em> are predicted to lose — on average — 17.8% of their highly suitable habitat areas, with two sites completely losing all their highly suitable habitat. In combination with increasing levels of coastal development and anthropogenic disturbance in Northwest Mexico, these predicted changes suggest that the potential future distribution of <em>C. c. roselaari</em> (and other shorebirds) will likely contract, exacerbating their ongoing population declines. Our results also make clear that SLR will likely have profound effects on ecosystems outside the north temperate zones, providing a clarion call to natural resource managers. Urgent action is required to begin securing sufficient space to accommodate the natural capacity of wetlands to migrate inland and implement local-scale solutions that strengthen the resilience of wetlands and human populations to SLR.</p>
Antarctica, a game changer for sea level rise
<p>How is Antarctica going to react to global warming? Right now, glaciers in the mountains and the Greenland and Antarctica ice sheets are all contributing roughly equally to sea level rise through melting, but in the near future, once Antarctica passes a tipping point, this might change completely, making sea level rise enough to wipe out all coastal cities of the world. However, if humanity manages to stay below +1,5°C, meaning following the Paris agreement, then we should be able to divide by two the contributions of land ice to global sea level by 2100.</p>
Galveston Sea Level Rise Scenarios
<p>Impacts of sea level rise (SLR) on galveston. Includes impacts for three infrastructure systems: buildings, electric power, and transportation. The folders are in this dataset:</p> <ul> <li>building-exposure: days per year of buildnig exposure.</li> <li>electricity-access: days per year of no electricity. </li> <li>galveston-exit: days per year with increases in travel time to the exit of galveston. </li> <li>utmb-hospital: days per year with increases in travel time to the UTMB hostpital</li> </ul> <p>In each folder there are five SLR scenarios; each of which has the median (50th percentile), 17th percentile, and 83rd percentile. </p> <p> </p>
Scenario divergence of sea level rise
<p>This file contains the underlying dataset for figures in Hinkel et al. (in review). The dataset contains probability density functions of mean and extreme sea level for a set of tide gauges from Gesla-2 (Woodworth et al., 2017) for projected regional sea level rise from two emission scenarios RCP2.6 and 8.5 as presented in the IPCC-AR5 report (Church et al., 2013). Based on the temporal change in overlap of the pdfs for both scenarios, a year of divergence is diagnosed for each tide gauge station.</p>
Data & Code for Sea-level rise induced threats depend on size of tide-influenced estuaries worldwide
<p>Data & Code for the article: "Data & Code for Sea-level rise induced threats depend on size of tide-influenced estuaries worldwide"</p> <p>- 1D Hydrodynamic model</p> <p>- Along-channel width profiles of the estuaries used in this study</p> <p>- Input data used for the tool and model in this study</p> <p>- Output data used for the figures</p>
Coastal marsh vulnerability to sea-level rise is exacerbated by plant species invasion
<p>In this dataset, it compasses the data and mat code file to visualize figures in the manuscript. </p>
Salt marsh litter quality and decomposition under sea-level rise scenarios: from leaves to fine absorptive roots
Open the record for dataset details and reuse information.
Wave setup and wave-induced flooding dataset for "Coastal flooding and sea-level rise allowances in atoll island"
<p>This dataset contains the wave setup and wave-induced flooding from the simulations in a profile of a coral reef island. The complete description of the simulations can be found in "Coastal flooding and sea-level rise allowances in atoll island". The wave setup and flooding values correspond to the median values of the 3 simulations performed for each parameter combination. A measure of the dispersion of the three simulation is also given as the standard deviation of the three simulations.</p>
Supporting data for: Coastal wetland adaptability to sea level rise: the neglected role of semi-diurnal versus diurnal tides
<p>Dataset necessary to reproduce the analyses and results presented in the paper.</p> <p>Belliard, J.-P., Gourgue, O., Govers, G., Kirwan, M.L., & Temmerman, S. (2022) Coastal wetland adaptability to sea level rise: the neglected role of semi-diurnal versus diurnal tides, submitted to Limnology & Oceanography Letters.</p> <p>The dataset contains:<br> • The source codes of the zero-dimensional numerical model, parametrized for the two model experiments, and the Matlab scripts to generate model input data (pre-processing).<br> • The scripts to perform geospatial analysis in order to select data from the various compiled and acquired (meta-)datasets and the scripts to perform subsequent multi-variate analyses applied to these selected data.<br> • The processed data that are reported and represented in figures in the associated manuscript.<br> • The Matlab scripts to generate the figures of the manuscript based on these processed data</p> <p>• The readme file detailing the contents of every data file, model subroutine and processing script. <br> • The metadata document<br> • The excel file "SET_dataset.xlsx" which contains all compiled SET records used in this study (see manuscript)<br> • The original reference (published sources) "SET_dataset_original_references.docx" associated with each SET record compiled.</p> <p> </p> <p> </p>
Sea-Level Rise in Southwest Greenland as a Contributor to Viking Abandonment
<p>Data and code to accompany the journal publication "Sea-Level Rise in Southwest Greenland as a Contributor to Viking Abandonment" in the Proceedings of the National Academy of Sciences of the United States of America (PNAS). This dataset includes data sets, computer codes and associated scripts, necessary to reproduce results in the article: (1) the surface computational grid; (2) model Greenland Ice Sheet history in time steps of 20 years from 1000 CE to present-day; (3) code to compute the relative sea-level change at any user specified set of sites across the same time steps; (4) code to map (2) and (3) on to conventional grids (e.g., latitude-longitude; Gauss-Legendre, etc.) based on a non-linear interpolation of data on a triangular grid via a second-order scheme; (5) code to convert sea level changes into time series of past topography from which flooding geometry (i.e., shoreline migration) can be tracked; and (6) code to integrate the flood geometries in (5) to compute time series of total flood area. More details in Readme.pdf.</p>
Data & Code for Peak water levels rise less than mean sea level in tidal channels subject to depth convergence by deepening
<p>Data & Code for the article: "Peak water levels rise less than mean sea level in tidal channels subject to depth convergence by deepening"</p> <p>- Input-files used to run the models</p> <p>- Scripts used for the figures</p> <p>- Excel-based tool to calculate tidal response to channel deepening with system-specific parameters</p> <p>- ...</p>
Supplementary Data Tables for 'The Global Drivers of Chronic Coastal Flood Hazards under Sea-Level Rise'
<p>Please refer to and cite the manuscript Hague et al. (2023), 'The Global Drivers of Chronic Coastal Flood Hazards under Sea-Level Rise', Earth's Future. https://doi.org/10.1029/2023EF003784 </p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.